[1]} introduced an end-to-end continuous ASR using a bidirectional recurrent neural network (RNN) encoder with an RNN decoder that aligns the input and the output sequences using the attention mechanism. The model achieved a word error rate (WER) of 18.57% on the TIMIT data set. [2]}, [3]} presented a state-of-the-art ASR system using E2E DNNs. They introduced a system that does not use any hand-designed language component, nor even the concept of "phoneme". Their result was achieved, as the authors stated in their original paper, through a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques and language models.
